The tracked dialog state, consisting of a representation of conversational context and a representation of KB retrieval results, is passed to the dialog policy to select the next system action.
In this section we first present a case study of a set of dialog policies for a movie-on-demand system [1]}, [2]}, showing how these policies are developed and their impact on the dialog system performance. Then, we introduce dialog acts and discuss dialog policy optimization methods using reinforcement learning.
